Journal
Journal of Applied Horticulture, 11(1):3-9, January-June, 2009
Appl
Applications of GIS to Citriculture in South Texas
Reginald S. Fletcher
United States Department of Agriculture, Agricultural Research Service, Kika de la Garza Subtropical Agricultural
Research Center, 2413 E. Hwy. 83, Weslaco, Texas 78596, E-mail: reginald.fletcher@ars.usda.gov
Abstract
The South Texas citrus industry needs an inventory of soil properties within existing citrus (Citrus spp.) orchards, wants data at the
county level showing soils that are suitable for citrus production, and would value any information related to the establishment of citrus
orchards. This study discusses integration of citrus, soil survey geographic data (SSURGO), and U.S. Census spatial and tabular data
with geographical information system (GIS) technology for citriculture. For this study, Hidalgo County Texas was evaluated because it
is the major citrus producing county in South Texas. The spatial and tabular data and commercial GIS software were used to inventory
selected soil chemical and physical properties within citrus groves, to identify orchards that may be affected by urban expansion, and
to select potential sites for establishing new citrus orchards. Results indicated that citrus, SSURGO, and U.S. census spatial and tabular
data integrated with GIS technology can be a powerful tool for citriculture. The information provided in this study should appeal to
producers, extension agents, scientists, and government agencies within the U.S. and abroad.
Key words: Citriculture, citrus, Citrus spp., Geographic Information System (GIS), soil survey geographic data (SSURGO), Topologically
Integrated Geographic Encoding and Referencing (TIGER) data, grapefruit, Citrus paradisi, oranges, Citrus sinensis
Introduction
In the Lower Rio Grande Valley of Texas, the citrus industry needs
inventories on chemical and physical properties of soils within
citrus groves to assist them in making management decisions.
The life expectancy of citrus trees ranges from 30 to 50 years.
Establishment of new groves on soils having a high potential for
citrus production and a low potential for commercial buildings
and homes would be ideal because these locations can remain
in citrus for extended periods, reducing the loss of established
orchards to urban expansion.
decisions in reclamation and in management of salt affected soils
in the Indo-Ganetic Plain. Boonyanuphap et al. (2004) employed
GIS-based analysis to identify suitable land for establishing
banana plantations in Thailand. Wu et al. (2001) showed that the
use of SSURGO data, integrated with remotely-sensed and other
GIS layers were beneficial for planning and for managing natural
resources in Finney County Kansas. Wu et al. (1997) also found
SSURGO data to be an excellent source for determining erodible
areas and for developing conservation practices. For subtropical
South Texas, Richardson et al. (1996) utilized GIS technology to
estimate sugarcane production, to map soil type, to manage farms,
to classify vegetation communities, to enforce the cotton stalk
destruction program, and to monitor the migration of Africanized
honey bees into the southern U.S.
Spatial and tabular soil data are available in digital formats,
allowing analysts to incorporate it into a geographic information
system (GIS), a computer system designed to store, retrieve, and
manipulate geographic data in various formats. The soil survey
geographic data (SSURGO) created by the Natural Resource
Conservation Service (NRCS) provides the most comprehensive
soil information for the United States (U.S.). It is designed for
multiple purposes (National Soil Survey Center, 1995). The U.S.
Census Bureau has also created spatial and tabular data in digital
format to support its mapping needs for the decennial census
and other bureau programs. This data contains line features
(e.g., roads, railroad, transportation, etc.,); boundary features
(e.g., statistical-census tracts and blocks, government-places and
counties, administrative-congressional districts, school districts,
etc.,); and landmark features (e.g., point-schools and churches,
area-park and cemeteries, and key geographic locations-apartment
building and factories).
Materials and methods
Researchers have used GIS technology and spatial and tabular data
provided by government agencies to assist them in assessing and
in solving problems affecting agricultural and natural resources.
Mandal and Sharma (2006) demonstrated the application of GIS
to develop maps of salt-affected soils in India at the regional, state,
and local level. Maps were useful for planning and for making
County description: Hidalgo County is located at the southern
tip of Texas (Fig. 1). The climate is subtropical. Annual rainfall
average is 58.42 cm. The elevation ranges from 12.2 to 114 m
with the greatest elevation occurring in the western half of the
county. The most valuable resources are soil, water, natural gas,
and the mild climate.
The development of SSURGO, along with other digital GIS
layers, has been an important advancement. Little information is
available on integrating SSURGO and U.S. Census data with GIS
technology for citriculture. The objectives of this study were to
use citrus, SSURGO, and U.S. Census data with GIS technology
(i) to inventory selected chemical and physical soil properties
within active citrus groves; (ii) to assess potential of soils for
establishing citrus orchards; and (iii) to address alternatives for
citrus grove establishment. For this study, Hidalgo County Texas
was evaluated because it is the major citrus producing county in
South Texas.
4
Applications of GIS to Citriculture in South Texas
(Sorghum bicolor L.), and sugarcane. Grapefruit (Citrus paradisi
L.) and oranges [C. sinensis (L.) Osbeck] are the primary citrus
grown in the county. Drainage and salinity are the major soil
problems affecting agriculture.
Development of GIS database: Illustrated in Fig. 2 is a flowchart
showing the development of the spatial and tabular citrus-soil
database. Spatial and tabular soil data of Hidalgo County, Texas,
and a spatial database template were downloaded from the Soil
Data Mart website (http://soildatamart.nrcs.usda.gov/), the
official NRCS website for SSURGO data. SSURGO is the most
comprehensive soil data in digital format provided by NRCS. The
spatial component contained information needed to display and
to analyze the data with GIS software. The tabular component
included a set of American Standard for Information Interchange
fields and text delimited files. The text delimited files matched a
table within the SSURGO database template, which is a Microsoft
Access database file containing empty tables. For this study, the
soildb_US_2002.mb template (Microsoft Access 2002 format)
was downloaded for further analysis. The spatial SSURGO data
were transferred to the Manifold GIS (version 7.0) software.
Fig. 1. (a) Map of the continental United States showing the location
of Texas and the study area (Enclosed in black square). (b) Close-up
showing the Lower Rio Grande Valley and the study area-Hidalgo
County.
Agriculture is a major source of income for the county. For
the state of Texas, the highest vegetable production occurs in
Hidalgo County. Irrigated land is primarily used for agricultural
production. Producers intensely farm the irrigated land, and their
methods are highly specialized. Irrigated crop production includes
cotton (Gossypium hirsutum L.), citrus, vegetables, grain sorghum
The template table and the text delimited data were merged
using Micrsoft Access 2002. The SSURGO database template
has a macro (a rule specifying how a certain input sequence
should be mapped to an output sequence according to a defined
procedure) that allows the user to import the text delimited data
into the SSURGO database. To use the macro and to complete
the importing procedure, the analyst has to use Microsoft Access.
Detailed instructions for this procedure were provided in the
SSURGO manual.
The majority of the feeder roots for mature citrus trees occur
within 61 cm of the soil surface (Ray and Walheim, 1980). For
the soil inventory, chemical and physical properties based on
this depth were determined with Soil Data Viewer (version 5.1,
Fig. 2. Flowchart showing steps used to create citrus soil database.
Applications of GIS to Citriculture in South Texas
http://nrcs.usda.gov), a freeware designed to create soil-based
thematic maps when linked with ArcMap (GIS software) and
tabular output for systems not equipped with ArcMap. The tabular
data was used in this study. Soil Data Viewer only works with
Microsoft Access databases, hence, the need to merge the text
with the tabular template using Microsoft Access.
To calculate the information needed for the 0 to 61 cm depth,
the soils data had to be aggregated. The following rules were
used to estimate the values: (1) the dominant component, (2) the
high tie break rule, and (3) converting null values to zeroes. The
dominant component aggregation method returns the attribute
value associated with the component occupying the highest
percent composition in the map unit. The tie-break rule indicates
which value should be selected from a set of multiple candidate
values, or which value should be selected in the event of a percent
composition tie. For this study, the component having the greatest
value was used in tie break situations. Interpret nulls as zeroes
is used to determine if a null value for a component should be
converted to zero before aggregation occurs. This conversion
happens only if a soil map unit has at least one component
where this value is not null. For this study, null values were not
interpreted as zeroes. Aggregated data were transferred to the GIS
software and incorporated into the soil spatial data file table.
Soil parameters estimated with the Soil Data Viewer were clay
content, pH, organic matter, and electrical conductivity. These
properties were selected because they are important to citrus
production. Brief definitions of the selected categories are as
follows. Clay is mineral material composed of particles that are
less than 0.002 mm in diameter. pH is a measure of the degree
of acidity or alkalinity of a soil. Soils with pH values below
6.6 are considered acidic; soils with pH values above 7.3 are
characterized as alkaline; and soils with pH values equal to or
within the extremes are characterized as neutral (USDA, 1981).
Electrical conductivity (EC) is a measure of the concentration of
water-soluble salts in soils. Organic matter is the plant and animal
residue in the soil at various stages of decomposition. Output
for soil properties were manually entered into the soil database
residing in the GIS.
Other soil parameters calculated with the Soil Data Viewer
included the soil map unit potential for building small commercial
buildings and for building dwellings without basements. The
dominant component and high tie break rule were used to assign
the map units to a class: not limited, somewhat limited, and very
limited.
Brief definitions for small commercial buildings and dwellings
without basements categories are as follows. Small commercial
buildings and dwellings without basements are structures less than
three stories high and do not have basements. Ratings are based on
soil properties that affect the capacity of the soil to support a load
without movement and on the properties that affect excavation
and construction costs. Properties influencing load-supporting
capacity include depth to a water table, ponding, flooding,
subsidence, linear extensibility (shrink-swell potential), and
compressibility (which is inferred from the Unified classification
of the soil).
Ratings are both verbal and numerical. Rating class terms
indicate the extent to which the soils are limited by all of the
5
soil features that affect the specified use. Verbal ratings include
the following classes, not limited, somewhat limited, and very
limited. Not limited encompasses soils having very favourable
characteristics for the specified use. Good performance and very
low maintenance can be expected. Somewhat limited includes
soils having features that are moderately favourable for the
specified use. The limitations can be overcome or minimized by
special planning, design, or installation. Fair performance and
moderate maintenance can be expected. Very limited indicates
that the soil has one or more features that are unfavourable for
the specified use. The limitations generally cannot be overcome
without major soil reclamation, special design, or expensive
installation procedures. Poor performance and high maintenance
can be expected.
No information related to the potential of soils for citrus grove
establishment appeared in the SSURGO tables. This information
was available in the hard copy version of the soil survey (USDA,
1981). Soils were grouped into the following classes for citrus
grove establishment, high, medium, low, questionable, or not
suitable. High potential indicated that the performance is at
or above the level of locally established standards, the cost of
measures for overcoming soil limitations are judged locally to
be favourable in relation to the expected performance or yields,
and soil limitations that continue after corrective measures are
installed do not detract appreciably from environmental quality or
economic returns. Medium potential signifies that production or
performance is somewhat below locally established standards, the
costs of measures for overcoming soil limitations are high, or soil
limitations that continue after corrective measures are installed
detract from environmental quality or economic returns. Low
potential indicates that production or performance is significantly
below local standards, measures that are required to overcome
soil limitations are very costly, or soil limitations that continue
after corrective measures are installed detract appreciably
from environmental quality or economic returns. Questionable
indicates that not enough evidence existed to determine if the
land could or could not be used for citrus grove establishment.
Unsuitable means that these soils are not adequate for citrus
grove establishment.
The soil survey did not group five soil map units into a potential
class. Several steps were taken to try to group the map units into
a class; the steps are as follows. The descriptions of the map units
were evaluated thoroughly and compared with map units having
the same surname. For example, the soil survey did not rate the
Harlingen clay-urban complex into a potential class for citrus
grove establishment. However, it did provide information on
Harlingen clay, which received a rating of not suitable for citrus
grove establishment. The Harlingen clay-urban complex consisted
of urban areas developed on Harlingen clay soil; therefore, the
Harlingen clay-urban complex was categorized as unsuitable for
citrus production.
If the nonrated soil could not be placed into a category using the
surname procedure, then it was assigned to one of two additional
classes, not rated-yield reported and not rated-yield not reported.
To create these classes, the Soil Data Viewer was used to determine
if citrus yields had been reported for the soil map unit in question.
If yield information was reported, then the soil was categorized
as not rated for citrus grove establishment-citrus yield reported
6
Applications of GIS to Citriculture in South Texas
(NRYR). If yield information was not provided, then the soil map
unit was rated as not rated for citrus grove establishment-no citrus
yield reported (NRNYR). The suitability data were transferred to
the soil spatial table residing in the Manifold software package.
Note: The spatial data utilized for the study were projected to a
coordinate system and datum by the various entities that created
the data. For this study, the final coordinate system and datum
used for the maps were Universal Transmercator (Zone 14 N)
and North American Datum 1983, respectively.
The citrus database was created in a joint venture between U.S.
Department of Agriculture-Animal Plant Inspection Service and
U.S. Department of Agriculture- Agricultural Research Service.
The 2006 updated version of the database was employed in this
study. It consists of spatial and tabular data. The former has
polygons representing areas planted to citrus trees and the latter
contains attributes for the citrus polygons. Spatial and tabular data
were imported directly into the GIS software (Fig. 2). Attributes
used in this study were citrus type and active orchards.
It was difficult to display the citrus polygons onto thematic maps.
For illustrative purposes, centroids (points) extracted from the
polygons of the citrus-soil spatial layer was used to represent the
locations of citrus groves.
To extract the urban area information of Hidalgo County, the
TIGER (Topologically Integrated Geographic Encoding and
Referencing)/Line 108th CD Census 2000 data were downloaded
from the U.S. Census Bureau website (www.census.gov). The
TIGER/Line files were created from the Census Bureau’s TIGER
database of selected geographic and cartographic information.
TIGER was developed by the U.S. Census Bureau to support
the mapping and related geographic activities required by the
decennial and economic censuses and sample survey programs.
The TIGER data were imported into the GIS software for further
analysis (Fig. 2).
For this study, information related to Urban Areas was employed
to assist in answering management questions. The Census Bureau
uses the term Urban Area (UA) to refer collectively to Urbanized
Areas (UZA) and Urban Clusters (UC). Urbanized Areas (UZA)
is a statistical geographic entity consisting of a central core and
adjacent densely settled territory that together contain at least
50,000 people, generally with an overall population density of
at least 386 people per square km. Urban Clusters (UC) is a new
statistical geographic entity designated by the Census Bureau
for the 2000 Census, consisting of a central core and adjacent
densely settled territory that together contains between 2,500
and 49,999 people. Typically, the overall population density is
at least 386 people per square km. Urban Clusters are based on
Census block and block group density and do not coincide with
official municipal boundaries.
Results and discussion
Citrus inventory by type: Summarized in Table 1 is the overall
statistics created with the combined citrus-soil database. The total
area encompassed by active orchards was 9214.7 ha. Grapefruit
and oranges were the dominant citrus types, covering 66% and
33% of the total area, respectively. Tangelos (C. x tangelo Ingram
and Moore), tangerines (C. reticula Blanco), and lemons [C. x
limon (L.) Burm.f.] were included in the citrus survey; their total
area of coverage was less than 1%.
Figs. 3-5 show how the GIS can be used to display the spatial
distribution of the orchards at the county level based on citrus
type. Grapefruit and orange groves were scattered throughout
the south central region of the county (Figs. 3 and 4). Lemons,
tangelos, and tangerines were established in isolated areas within
the county (Fig. 5).
Soil physical data of citrus groves: The soil physical data
indicated that producers established their groves on soils with
high potential for citrus grove establishment (Table 1). Thirty-four
percent of citrus production occurred on soils with lower ratings.
The dominant soil mapping units for citrus grove establishment
was Brennan fine sandy loam, 0 to 1% slopes; followed by
Hidalgo sandy clay loam, 0 to 1% slopes; Hidalgo fine sandy
The soil data was merged with the citrus data by employing the
topology overlay function of the GIS software. This function
created new polygons containing soil and citrus attributes. Using
the newly created data layer and structure query language, the
inventory of the selected chemical and physical properties of soils
within citrus groves were completed. Structure query language
and visual basic script were used throughout the study to select
appropriate information from tables, estimate area, or perform
mathematical calculations.
At the county level, a thematic map showing the potential of
the soil map units for citrus grove establishment was developed
to assist in selecting new areas for establishing citrus groves. A
thematic map showing the census urban area data, citrus location
data, and high potential soil citrus grove establishment data versus
the building site development data was evaluated to determine
alternative sites for establishing citrus groves.
Fig. 3. Spatial distribution of active grapefruit orchards within Hidalgo
County.
Applications of GIS to Citriculture in South Texas
7
Table 1. Overall statistics for active citrus groves in Hidalgo County, Texas, including area of citrus by variety, soil map unit ranked by area*, soil
clay content ranked by area*, soil organic matter ranked by area*, soil potential, soil pH level, soil electrical conductivity, and soil characteristics for
building site development (small commercial buildings and dwellings without a basement)
Area (ha)
Electrical Conductivity
Value Range (S m-1)
Non-saline
0.00 to 0.20
9151.0
Slightly-saline
0.21 to 0.40
45.6
Moderately saline
0.41 to 0.80
1.0
Strongly-saline
0.81 to 1.60
17.1
pH
Slightly acid
Neutral
Mildly alkaline
Moderately alkaline
Strongly alkaline
pH Values
6.1 to 6.5
6.6 to 7.3
7.4 to 7.8
7.9 to 8.4
8.5 to 9.0
Potential
High potential
Medium potential
Low potential
Not suitable
Questionable
NRYR
NRNYR
Area (ha)
8033.2
597.6
355.2
163.1
17.3
48.4
0.0
Clay g kg-1
193
224
203
256
180
Area (ha)
270.7
274.5
2342.1
2363.1
2889.2
Area (ha)
41.1
753.5
2967.5
5452.1
0.5
Building site development
Not limited
Somewhat limited
Very limited
Organic matter g kg-1
15.3
5.7
16.9
6.6
16.3
Variety
Grapefruit
Oranges
Tangelo
Tangerine
Lemon
Area (ha)
3480.0
5240.0
494.2
Area (ha)
266.6
276.6
401.9
2893.2
4705.1
Area (ha)
6124.5
3045.1
38.1
6.8
0.2
Soil Map Unit
Area (ha)
Raymondville clay loam
266.5
Brenan fine sandy loam, 1 to 3% slopes
331.6
Hidalgo fine sandy loam, 0 to 1% slopes
2271.5
Hidalgo sandy clay loam, 0 to 1% slopes
2351.9
Brennan fine sandy loam, 0 to 1% slopes
2557.6
*Only the top five soils by area are included in the table. NRYR = Not rated yield reported and NRNYR = not rated no yield reported.
loam, 0 to 1% slopes; Brennan fine sandy loam, 1 to 3% slopes;
and Raymondville clay loam (Table 1). Raymondville clay loam
ranked number five in area listed under citrus production. It had
a low potential rating for citrus grove establishment; while the
top four soils had a high potential ranking.
and micronutrients such as copper, iron, zinc, and manganese
are not readily available for plant uptake. Therefore, producers
have to supply these nutrients to the trees. Macronutrients such
as calcium, magnesium, and potassium would normally be high
in these soils.
Most of the groves appeared on soils that were categorized as
somewhat limited to building development, followed by not
limited and very limited (Table 1). Groves occurring on soils rated
not limited and somewhat limited for building site development
may succumb to urban expansion in the future.
The statistics indicated that producers have chosen to grow citrus
trees on non-saline soils (0 to 0.2 S m-1), limiting the effect of
soil salinity on citrus yields. Organic matter content ranged from
0.5 to 20 g kg-1 with the most hectares of citrus appearing on
soils having 16.3 g kg-1 of organic matter. Organic matter totals
seemed to fluctuate; for example, in comparison of the top soils
by area for organic matter content, the values ranged from 6.6 g
kg-1 to 16.3 g kg-1. It is well known by producers in this county
that they have to increase the organic matter content in soils used
for agricultural production.
Clay content ranged from 70 to 455 g kg-1. Increases in clay content
represent heavier textured soils. Eighty-two percent of the groves
occurred on soils having clay contents of 186, 203, and 256 g kg-1.
Shrink swell capacity of the soils played a role in determining if
they were appropriate for agricultural production and for building
site development. There was not a pattern between clay content
and potential rating for establishing citrus groves.
Soil chemical data-citrus groves: Citrus production occurred on
strongly alkaline (pH values: 8.5-9.0) to slightly acid soils (pH
values: 6.1-6.5) (Table 1). The majority of the production was
established on moderately alkaline soils (pH values: 7.9-8.4; Table
1). At pH values above 7.8, macronutrients such as phosphorus
Spatial distribution of soil potential at the county level for
citrus grove establishment: Fig. 6 shows the spatial distribution
of the soil potential for active citrus grove establishment and
the location of citrus groves within the county. Soils with high
potential for citrus grove establishment occupy most of the county.
Based on this map, producers can expand citrus production into
the north central section of the county. If they have to settle for
8
Applications of GIS to Citriculture in South Texas
Fig. 4. Spatial distribution of active orange orchards within Hidalgo
County.
Fig. 6. Thematic map showing the soil potential for citrus grove
establishment and the location of active citrus groves based on citrus
polygons and soil interactions in Hidalgo County.
Fig. 5. Spatial distribution of active lemon, tangelo, and tangerine
orchards in Hidalgo County.
using soils with medium potential for citrus grove establishment,
then areas in the extreme western and the central north east section
of the county are adequate. Soils having low potential would be
their last alternative. These areas are scattered throughout the
county.
Fig. 7. Thematic map showing the high soil potential for citrus grove
establishment vs soil potential for building site development, urban
area identified by the 2000 census, and the location of citrus groves
based on citrus polygons and soil interactions in Hidalgo County. HPC
= high potential for citrus grove establishment, NLB = not limited for
building site development, and SWLB = somewhat limited for building
site development.
Spatial distribution of high soil potential for citrus vs building
site development, urban area, and citrus grove location:
A thematic map showing high potential soils for citrus grove
establishment versus the building development categories are
shown in Fig. 7. High potential soils were chosen for map
development because they were the soils of choice for growers
to establish citrus groves (Table 1). Combinations mapped were
high potential for citrus grove development and some what
Applications of GIS to Citriculture in South Texas
limited potential for building site development (92300 ha) and
high potential for citrus grove establishment and the some what
limited for building development combination (71300 ha). None
of the soils were classified into the high potential for citrus
grove establishment and very limited potential for building site
development class. Soils ranked into this category would have
been an ideal choice for establishing citrus groves. Results specify
that prime land for citrus grove establishment can have a rank of
not limited or somewhat limited for building site development.
The urban area 2000 census map overlaid onto the citrus and
soil layers was helpful in indentifying citrus groves appearing in
urban areas (Fig. 7). Thirty-four percent of the citrus production
occurred in urban areas. In these areas, buildings are continuously
being built because of the increase of population. Therefore,
some of these groves may be lost to commercial building over
time. In the future, citrus producers may have to get accustomed
to the concept of urban farming, the growing, processing, and
distribution of food through intensive plant cultivation in and
around cities (Bailkey and Nasr, 1999). This practice could limit
treatments commonly used to control pests in citrus orchards.
To reduce potential losses to urban expansion, it is believed
that new groves should be established as far as possible from
the urban area boundaries. It appears that the northwest central
and the northeast central regions of the county would be ideal
choices for establishing new groves. The overall results of this
study concurred with others in that GIS technology is valuable
for assessing and for managing agricultural resources (Richardson
et al., 1996; Boonyanuphap et al., 2004; Mandal and Sharma,
2006).
Results of this study showed that citrus, SSURGO, and U.S.
census spatial and tabular data integrated with GIS technology
can be a powerful tool for citriculture. This information was
employed to inventory soil chemical and physical properties
9
within citrus groves of Hidalgo County, Texas, to estimate and to
identify orchards that may be affected by urban expansion, and
to select potential sites for establishing new citrus orchards. The
techniques used in this study and findings of the study should
appeal to the citrus industry in the U.S. and abroad.
Acknowledgments: I thank Dr. David Bartells and Mr. Russel
Sheets for supplying the citrus database.
References
Bailkey, M. and J. Nasr, 1999. From brown fields to green fields:
Producing food in North American cities. Community Fd. Security
News, Fall 1999/Winter 2000: 7.
Boonyanuphap, J., D. Wattanachaiyingcharoen and K. Sakurai, 2004.
GIS-based land suitability assessment for Musa (ABB group)
plantation. J. Appl. Hort., 6(1): 3-10.
Mandal, A.K. and R.C. Sharma, 2006. Computerized database of saltaffected soils for agro-climatic regions in the Indo-Gangetic Plain
of India using GIS. Geocarto Intl., 21(2): 47-57.
National Soil Survey Center, 1995. Soil Survey Geographic (SSURGO)
Data Base: Data Use Information. USDA NRCS Misc. Publ. no.
1527. Natural Resources Conservation Serv., Fort Worth, TX.
Ray, R. and L. Walheim, 1980. Citrus, How to Select, Grow, and Enjoy.
Hort. Publishing Co., Inc. Tucson, AZ.
Richardson, A.J., C.L. Wiegand, G.L. Anderson and A.H. Gerbermann,
1996. Six exemplary applications of GIS technology to subtropical
Texas agriculture and natural resources. Gecarto Intl., 11(1): 4960.
USDA. 1981. Soil Survey of Hidalgo County, Texas. U.S. Govt. Printing
Office, Washington, D.C.
Wu, J., M.D. Nellis, M.D. Ransom, K.P. Price and S.L. Egbert, 1997.
Evaluating soil properties of CRP land using remote sensing and GIS
in Finney County, Kansas. J. Soil Water Conserv., 52: 352-358.
Wu, J., M.D. Ransom, G.J. Kluitenberg, M.D. Nellis and H.L. Seyler,
2001. Land-use management using a soil survey geographic database
for Finney County, Kansas. Soil Sci. Soc. Amer. J., 65: 169-177.